Previous works on self-supervised learning in robot perception mainly focus on learning object segmentation [1]}, object detection [2]}, and dense pixel-wise correspondences [3]}, [4]}, since it is relatively easy to obtain annotations in 2D image space.
In this work, we explore self-supervised learning for 6D object pose estimation. Our system benefits from the recent advances in 6D object pose estimation using deep neural networks [5]}, [6]}, [7]}. By incorporating these techniques into the interactive robot perception framework [8]}, we can obtain accurate 6D poses of objects to improve the system.
